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Kumar, Neeraj,Misra, Sudip,Chilamkurti, Naveen,Jong-Hyouk Lee,Rodrigues, Joel J. P. C. IEEE 2016 IEEE transactions on dependable and secure computi Vol.13 No.1
<P>In recent times, Plug-in Electric Vehicles (PEVs) have emerged as a new alternative to increase the efficiency of smart grids (SGs) in a vehicles-to-grid (V2G) environment. The V2G environment provides a bidirectional power and information flow, so that users can have an optimized usage as per their requirements. However, uncontrolled and unmanaged power distribution may lead to an overall performance degradation in V2G environment. One reason for this uncontrolled and unmanaged flow may be due to the usage of power by unauthorized users. To address this issue, we propose a Bayesian Coalition Negotiation Game (BCNG) as a utility for secure energy management for PEVs in the V2G environment. We have used a BCNG along with Learning Automata (LA), wherein LA are stationed on PEVs and are assumed as the players in the game. To provide an approach based on resilience for any misuse of electricity consumption, a new Secure Payoff Function (SPF) is proposed. The players take actions and update their action probability vector using the SPF. A Nash Equilibrium (NE) is also achieved in the game using convergence theory. Our proposal is evaluated with various metrics. The proposed scheme also provides mutual authentication and resilience against various attacks during power distribution.</P>
Machine Learning Techniques for Speech Recognition using the Magnitude
Krishnan, C. Gopala,Robinson, Y. Harold,Chilamkurti, Naveen Korea Multimedia Society 2020 The journal of multimedia information system Vol.7 No.1
Machine learning consists of supervised and unsupervised learning among which supervised learning is used for the speech recognition objectives. Supervised learning is the Data mining task of inferring a function from labeled training data. Speech recognition is the current trend that has gained focus over the decades. Most automation technologies use speech and speech recognition for various perspectives. This paper demonstrates an overview of major technological standpoint and gratitude of the elementary development of speech recognition and provides impression method has been developed in every stage of speech recognition using supervised learning. The project will use DNN to recognize speeches using magnitudes with large datasets.
Message Aggregation in VANETs for Delay Sensitive Applications
Sanket Desai,Rabee Elhdad,Naveen Chilamkurti 보안공학연구지원센터 2015 International Journal of Smart Home Vol.9 No.10
A Vehicular Ad-Hoc Network (VANET) is categorized as a Mobile Ad-Hoc Network (MANET) which delivers wireless network servies with an aim to improve road safety and enhance driving comfort. Diverse applications of Vehicular Ad-Hoc Networks such as infotainment, road safety and public safety have made VANETs as a notable and emerging area of research and development. As of now, numerous vehicular ad-hoc network research projects have been mainly aimed at data security and routing. This has raised a critical problem of data congestion and loss of data accuracy in VANETs. A major challenge in VANETs is to provide efficient data communication and propogation for precise and valuable information. This paper presents a generalized framework for message aggregation. Message Aggregation can be used to transmit minimal data and to enhance the communication efficiency thus reducing the communication overhead in VANETs. This will help in reducing the redundancy in VANETs resulting in dissemination of precise information
Embedded deep vision in smart cameras for multi-view objects representation and retrieval
Ahmad, Jamil,Mehmood, Irfan,Rho, Seungmin,Chilamkurti, Naveen,Baik, Sung Wook Elsevier 2017 Computers & electrical engineering Vol.61 No.-
<P>Active large scale surveillance of indoor and outdoor environments with multiple cameras is becoming an undeniable necessity in today's connected world. Enhanced computational and storage capabilities in smart cameras establish them as promising platforms for implementing intelligent and autonomous surveillance networks. However, poor resolution, limited number of samples per object, and pose variation in multi-view surveillance streams, make the task of efficient image representation highly challenging. To address these issues, we propose an efficient and powerful convolutional neural network (CNN) based framework for features extraction using embedded processing on smart cameras. Efficient, high performance, pre-trained CNNs are separately fine-tuned on persons and vehicles to obtain discriminative, low dimensional features from segmented surveillance objects. Furthermore, multi-view queries of surveillance objects are used to improve retrieval performance. Experiments reveal better efficiency and retrieval performance in different surveillance datasets. (C) 2017 Elsevier Ltd. All rights reserved.</P>
Real-time secure communication for Smart City in high-speed Big Data environment
Rathore, M. Mazhar,Paul, Anand,Ahmad, Awais,Chilamkurti, Naveen,Hong, Won-Hwa,Seo, HyunCheol Elsevier 2018 Future generation computer systems Vol.83 No.-
<P><B>Abstract</B></P> <P>The recent development in the technology brings the concept of Smart City that is achieved through real-time city related intelligent decisions by analyzing the data harvested from various smart systems in the city using millions of sensors and devices connected over the Internet, termed as Internet of Things (IoT). These devices generate the overwhelming volume of high-speed streaming data, termed as Big Data. However, the generation of city data at a remote location and then transmitting it to central city servers for analysis purpose raises the concerns of security and privacy. On the other hand, providing security to such Big Data streaming requires a high-speed security system that can work in a real-time environment without providing any delay that may slow down the overall performance of the Smart City System. To overthrown these challenges, in this paper, we proposed an efficient and real-time Smart City security system by providing strong intrusion detection at intelligent city building (ICB) and also a security protocol to protect the communication between the remote smart system(RSS)/User and the city analysis building, i.e., ICB. The proposed communication security protocol consists of various phases, i.e., registration phase, session key exchange phase, session key revocation phase, and data transmission phases from RSS to ICB as well as from User to ICB. Vast security analyses are performed to evaluate the credibility of the system. The proposed system is also evaluated on efficiency in terms of computation cost and throughput of overall functions used in the system. The system’s evaluation and the comparative study with existing system show that the prosed system is secure, more efficient, and able to work in a real-time, high-speed Smart City environment.</P> <P><B>Highlights</B></P> <P> <UL> <LI> This paper presents a system architecture that integrate Smart City with technical network. </LI> <LI> A Novel notion of Smart-City Network is defined by Communication Security. </LI> <LI> Intelligent Smart Building Architecture with Remote Smart System. </LI> <LI> The system is also implemented of Smart City Decision are done on top of Hadoop parallel nodes. </LI> </UL> </P>
( Guangjie Han ),( Huihui Xu ),( Jinfang Jiang ),( Lei Shu ),( Naveen Chilamkurti ) 한국인터넷정보학회 2012 KSII Transactions on Internet and Information Syst Vol.6 No.11
Recently there has been an increasing interest in exploring the radio irregularity research problem in Wireless Sensor Networks (WSNs). Measurements on real test-beds provide insights and fundamental information for a radio irregularity model. In our previous work “LMAT”, we solved the path planning problem of the mobile anchor node without taking into account the radio irregularity model. This paper further studies how the localization performance is affected by radio irregularity. There is high probability that unknown nodes cannot receive sufficient location messages under the radio irregularity model. Therefore, we dynamically adjust the anchor node`s radio range to guarantee that all the unknown nodes can receive sufficient localization information. In order to improve localization accuracy, we propose a new 2-hop localization scheme. Furthermore, we point out the relationship between degree of irregularity (DOI) and communication distance, and the impact of radio irregularity on message receiving probability. Finally, simulations show that, compared with 1-hop localization scheme, the 2-hop localization scheme with the radio irregularity model reduces the average localization error by about 20.51%.